{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:46:11Z","timestamp":1783439171959,"version":"3.54.6"},"reference-count":136,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T00:00:00Z","timestamp":1604966400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005306","name":"Alfried Krupp von Bohlen und Halbach-Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005306","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Marie Sk\u0142odowska-Curie","award":["813533"],"award-info":[{"award-number":["813533"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,22]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein\u2013protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.<\/jats:p>","DOI":"10.1093\/bib\/bbaa257","type":"journal-article","created":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T19:10:56Z","timestamp":1599851456000},"page":"1515-1530","source":"Crossref","is-referenced-by-count":238,"title":["Biological network analysis with deep learning"],"prefix":"10.1093","volume":"22","author":[{"given":"Giulia","family":"Muzio","sequence":"first","affiliation":[{"name":"Machine Learning and Computational Biology Lab at ETH Z\u00fcrich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leslie","family":"O\u2019Bray","sequence":"additional","affiliation":[{"name":"Machine Learning and Computational Biology Lab at ETH Z\u00fcrich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karsten","family":"Borgwardt","sequence":"additional","affiliation":[{"name":"Life Sciences at ETH Z\u00fcrich"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,11,10]]},"reference":[{"issue":"4","key":"2021032314263185500_ref1","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.molcel.2015.05.004","article-title":"High-throughput sequencing technologies","volume":"58","author":"Reuter","year":"2015","journal-title":"Mol Cell"},{"key":"2021032314263185500_ref2","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"2021032314263185500_ref3","volume-title":"Beyond regression: new tools for prediction and analysis in the behavioral sciences","author":"Werbos","year":"1974"},{"key":"2021032314263185500_ref4","article-title":"Learning logic technical report tr-47","volume-title":"Center of Computational Research in 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